Paste your list
Add values separated by newlines, commas, semicolons, or tabs.
Remove duplicates from a list separated by newlines, commas, semicolons, or tabs. Keep the first occurrence, ignore case, and count repeats locally.
Separate values with newlines, commas, semicolons, or tabs — one per line works best.
Three simple steps, with your content kept on your device.
Add values separated by newlines, commas, semicolons, or tabs.
Toggle case-insensitive matching and duplicate counting.
Keep the first occurrence of every value and copy or download the result.
Fast, focused, and made to be clear on every screen.
Duplicates are removed while the original order is preserved.
Case-insensitive comparison and per-value duplicate counts.
Deduplication runs in your browser. Nothing is uploaded.
Duplicates sneak into lists from many places: copied spreadsheet columns, merged mailing lists, exported tags, and pasted data. A list deduplicator removes the repeated values so you work with a clean set.
Cleaning duplicates matters in practice: email campaigns go to each address once, inventory counts are accurate, and reports stop double-counting rows. Deduplicate list operations are a daily task in many workflows.
To remove duplicate values online, paste your list — one value per line, or separated by commas, semicolons, or tabs — and run the tool. The first occurrence of every value is kept and the rest are dropped.
Enable Ignore case to treat Apple and apple as the same value, and Count duplicates to see how many times each value appeared in the original input before removal. The result is a clean list unique values only, ready to use.
Values are compared exactly by default, so Apple and apple are different entries until you switch on case-insensitive matching. Whitespace around a value is significant unless trimmed first. The output is a list of unique values in their original order.
The tool preserves the original order of first appearances. That means dedupe text list output keeps your list's structure — the first time a value appears stays in place, and later copies vanish.
Common uses include cleaning email lists before a send, removing repeated product names from an inventory export, merging contact lists from two sources, and preparing a list of unique values for a database import.
The stats panel makes the result visible at a glance: input values, unique values, and how many were removed. Copy the clean list or download it as a text file.
Lists often contain personal data such as emails, phone numbers, or customer records. This tool processes everything locally in your browser — nothing is uploaded, and there is no account to sign up for.
The deduplicator works offline once loaded and handles large lists instantly, with a clear input limit to protect your browser's memory.
Deduplication is one part of list hygiene. The Duplicate Line Remover does the same job line by line, and the Line Sorter orders your clean list alphabetically.
The Text Frequency Analyzer counts how often each value or word appears, and the CSV Cleaner removes duplicate rows from tabular data before you load it into a spreadsheet.
A common scenario is a mailing list export with the same subscriber appearing twice. Pasting the list and deduplicating leaves one row per address, which prevents double sends and keeps metrics accurate.
E-commerce teams dedupe product ID lists before a price update so no item is processed twice. Inventory exports, event attendee lists, and tag clouds all benefit from the same quick cleanup.
When Count duplicates is on, the tool doubles as a mini frequency counter: it shows exactly how many times each value appeared, which reveals data-quality problems like the same customer recorded three times under slightly different spellings.
Case sensitivity is the decision that matters most. Keep it off for names and words, where Ada and ada are clearly the same; keep it on for codes and identifiers, where AdA-1 and ada-1 may be genuinely different values.
Delimiter choice follows the source: newlines for pasted columns, commas for inline lists, semicolons for European-style data, and tabs for spreadsheet exports.
The removed count in the stats panel tells you whether the input was actually dirty: zero removed means the list was already clean, and a large number signals the source export needs attention.
The first occurrence of each value is kept, preserving the original order of the list.
It treats values such as Apple and apple as the same item while keeping the text of the first occurrence in the output.
It appends how many times each value appeared in the input, for example apple (3), so you can see how frequent each item is.